الفهرس 
IntroductionOnly 14 pages are availabe for public view
 |  | from | 132 |  |  |
| | | from | 132 | | |
Abstract
Power transformers are an expensive critical asset in power networks.
Most of the transformers currently in service worldwide are approaching
or have already exceeded their design life, consequently, power utilities
face a significant risk of power transformer failure.
Frequency response analysis (FRA) is the most reliable technique for
detecting any mechanical deformation within the transformer. Since FRA
depends on graphical analysis, its signature interpretation is still a great
challenge that requires a highly specialized person to predict the type of
fault. This may lead to different interpretation for the same FRA signature
by different experts. Moreover, the current FRA technique cannot detect
minor winding deformations.
In this research different types of transformer faults are simulated using
MATLAB by changing some electrical parameters in the high frequency
transformer model. This simulation is carried out on three different
transformers to investigate the changes in the FRA signature under
various faults. Also, the whole frequency band (10 Hz – 1 MHz) is
divided into four regions and some statistical parameters are calculated
for each region to investigate the change in these parameters under
various faults. These statistical parameters are used as input data for an
artificial intelligence model for identifying the fault type.
The used model is called adaptive neuro-fuzzy inference system
(ANFIS). The ANFIS has the advantages of both fuzzy systems which
can incorporate the experience knowledge into a set of rules and the
neural networks that can adapt their parameters.
The proposed ANFIS model is applied on the three simulated
transformers to test its ability of fault identification.